Abstract
Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task k (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.- Anthology ID:
- 2024.clinicalnlp-1.32
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 346–352
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.32
- DOI:
- Cite (ACL):
- Nadia Saeed. 2024. MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 346–352, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch (Saeed, ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.32.pdf